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Related Experiment Video

Updated: Sep 18, 2025

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Gaia: An AI-enabled genomic context-aware platform for protein sequence annotation.

Nishant Jha¹, Joshua Kravitz¹, Jacob West-Roberts¹

¹Tatta Bio, Cambridge, MA 02142, USA.

Science Advances

|June 20, 2025

Summary

Gaia (Genomic AI Annotator) is a new tool for protein sequence similarity search. It uses genomic context to find functionally related genes missed by other methods, improving biological research.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Protein sequence similarity search is crucial in biology.
Current methods often neglect genomic context, limiting functional insights, especially in microbes.
Identifying functionally related genes in conserved genomic contexts is challenging.

Purpose of the Study:

To introduce Gaia (Genomic AI Annotator), a novel platform for rapid, context-aware protein sequence search.
To leverage genomic context for improved identification of functionally related genes.
To provide a freely available web-based tool for microbial genomic analysis.

Main Methods:

Developed Gaia, a sequence annotation platform utilizing a mixed-modality genomic language model (gLM2).
Trained gLM2 on amino acid sequences and their genomic neighborhoods to generate integrated embeddings.
Enabled real-time search across a database of over 85 million protein clusters from 131,744 microbial genomes.

Main Results:

Gaia's embeddings integrate sequence, structure, and genomic context information.
Identified functionally and/or evolutionarily related genes in conserved genomic contexts.
Demonstrated superior homolog retrieval performance compared to traditional embedding and alignment-based methods.

Conclusions:

Gaia enhances protein sequence search by incorporating crucial genomic context.
The platform facilitates the discovery of gene relationships potentially missed by conventional approaches.
Gaia offers a valuable, accessible resource for microbial genomics research.